STT
spaCy
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| STT | spaCy | |
|---|---|---|
| 11 | 106 | |
| 2,131 | 28,704 | |
| 2.7% | 1.3% | |
| 0.6 | 9.2 | |
| about 2 months ago | 7 days ago | |
| C++ | Python | |
| Mozilla Public License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
STT
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Rest in Peas: The Unrecognized Death of Speech Recognition (2010)
What has happened since then? I know Common Voice has come and gone https://en.wikipedia.org/wiki/Common_Voice https://github.com/coqui-ai/STT
And I've seen some neural approaches too
No idea where to look for comparisons though.
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Numen - FOSS voice control for handsfree computing
I basically just used coqui stt https://github.com/coqui-ai/STT
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Are there any OCR and Speech-to-Text services that are privacy friendly?
This speech-to-text works well: https://github.com/coqui-ai/STT. openai's "whisper" is probably better but I haven't tried it: https://towardsdatascience.com/transcribe-audio-files-with-openais-whisper-e973ae348aa7
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Introducing Whisper
I use two SST to live-translate audio that I listen to so I can look back (in paragraph form) to see things that I or the youtube has previously said: https://github.com/coqui-ai/STT https://github.com/ratwithacompiler/OBS-captions-plugin
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You can now tether any prod Vector to Wire's Open Source Escape Pod • thedroidyouarelookingfor
I did have to install Coqui STT and go-asticoqui manually before i was able to run Chipper.
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Currently working on a custom Virtual Assistant ('Randy') to help automate things in my shed (mainly CNC equipment) and also perform basic tasks. This morning I was able to get it to publish events on my google calendar.
What do you use as STT? I have heard good things about coqui (https://github.com/coqui-ai/STT) and will use it for my Assistant-build.
- Speech to Text Best Resource
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I put together a tutorial and overview on how to use DeepSpeech to do Speech Recognition in Python
If anyone is looking for a maintained version of DeepSpeech, checkout Coqui's repositories for STT and TTS. Coqui is lead by the engineers that used to work on DeepSpeech at Mozilla.
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CoquiTTS: 🐸💬 - Open Source Text-to-Speech framework.
Link: https://github.com/coqui-ai/STT
- Mozilla Common Voice Adds 16 New Languages and 4,600 New Hours of Speech
spaCy
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Step by step guide to create customized chatbot by using spaCy (Python NLP library)
Hi Community, In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):
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Best AI SEO Tools for NLP Content Optimization
SpaCy: An open-source library providing tools for advanced NLP tasks like tokenization, entity recognition, and part-of-speech tagging.
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Who has the best documentation you’ve seen or like in 2023
spaCy https://spacy.io/
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A beginner’s guide to sentiment analysis using OceanBase and spaCy
In this article, I'm going to walk through a sentiment analysis project from start to finish, using open-source Amazon product reviews. However, using the same approach, you can easily implement mass sentiment analysis on your own products. We'll explore an approach to sentiment analysis with one of the most popular Python NLP packages: spaCy.
- Retrieval Augmented Generation (RAG): How To Get AI Models Learn Your Data & Give You Answers
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Against LLM Maximalism
Spacy [0] is a state-of-art / easy-to-use NLP library from the pre-LLM era. This post is the Spacy founder's thoughts on how to integrate LLMs with the kind of problems that "traditional" NLP is used for right now. It's an advertisement for Prodigy [1], their paid tool for using LLMs to assist data labeling. That said, I think I largely agree with the premise, and it's worth reading the entire post.
The steps described in "LLM pragmatism" are basically what I see my data science friends doing — it's hard to justify the cost (money and latency) in using LLMs directly for all tasks, and even if you want to you'll need a baseline model to compare against, so why not use LLMs for dataset creation or augmentation in order to train a classic supervised model?
[0] https://spacy.io/
[1] https://prodi.gy/
- Swirl: An open-source search engine with LLMs and ChatGPT to provide all the answers you need 🌌
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How to predict this sequence?
spaCy
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What do you all think about (setq sentence-end-double-space nil)?
I chose spacy. Although it's not state of the art, it's very well established and stable.
- spaCy: Industrial-Strength Natural Language Processing
What are some alternatives?
DeepSpeech - DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.
TextBlob - Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
NeMo - A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
NLTK - NLTK Source
vosk-api - Offline speech recognition API for Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
TTS - :robot: :speech_balloon: Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts)
polyglot - Multilingual text (NLP) processing toolkit
OBS-captions-plugin - Closed Captioning OBS plugin using Google Speech Recognition
textacy - NLP, before and after spaCy